Neural Networks for Pattern Recognition
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Evolution Strategy in Portfolio Optimization
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HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
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EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
Parallel CHC algorithm for solving dynamic traveling salesman problem using many-core GPU
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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This paper presents an approach to extracting stock market trading rules from stock market data. Trading rules are based on two multi-layer perceptrons, one generating buy signals and one generating sell signals. Inputs of these perceptrons are fed with values of technical indicators computed on historical stock quotations. Results of a large number of experiments on real-life data from the Paris Stock Exchange confirm that the model of trading rules is reasonable and the trading rules are able to generate reasonable trading signals, not only over a training period, used in the training process, but also over a test period, unknown during constructing trading rules. Moreover, trading strategies defined by such trading rules are profitable and often outperform the simple Buy&Hold strategy.